As needs for freight and passenger transportation is growing over vast area, it is resulting in increasing demands for efficient and larger size railway networks. The large size railway networks have large numbers of stations and connecting the stations with thousands of trains moving on multiple tracks. In the real world, the continuous monitoring and re-planning of the large number of trains in the large railway network is a complex process. Further generation of high-quality, feasible and safe train schedules in the large railway network are extremely hard. In typical scenarios, large numbers of human resources or train dispatchers are engaged in continuously monitoring and controlling of the thousands of trains over the vast networks. Unless the train dispatchers can react rapidly and effectively to mitigate continuous deviations and disruptions, the economic viability of the highly capital-intensive railway industry is adversely impacted.
Train dispatching is of crucial importance in the operations of a railway network because sub-optimal dispatching decisions regarding meeting and passing of the trains greatly degrade throughput, transit times and on-time performance. Dispatching decisions taken with limited local knowledge of railway network adversely impact performance at the overall railway network level. Rail companies differ on relative importance of tactical versus operational planning. The unpredictability of deviations and disruptions on top of day-to-day variability in traffic patterns, often make tactical traffic planning appear like a futile exercise. According to one study, 45% of variance of train arrival times is due to variance in over-the-line transit times. Unfortunately, dispatchers neither have nor can cognitively use the complete network wide information and thus dispatcher's decisions are local and not holistic. The dispatchers locally avoid delaying higher priority trains, often clearing lower priority trains into sidings far in advance of incoming high-priority trains without consideration for network-wide effects. The dispatchers generally use the same heuristics even in abnormal conditions of network congestion and periods of dense traffic, when this strategy can often backfire as delaying a cluster of low priority trains may increase the congestion in which soon all the trains are delayed regardless of the priority of the trains; affecting overall performance of the railway network.
Hence, while the management of large size railway networks needs meticulous planning, the complexity of doing so for large size railway networks may rise uncontrollably with increases in the numbers of stations, sections, trains, and the like. Prior art solutions for railway planning and scheduling fall short in providing efficient management of the trains in such large size railway networks. A number of solutions are proposed in the prior art for automated train planning and scheduling, but all the solutions are restricted to limited numbers of trains and stations. These conventional methods for the railway planning and scheduling handle limited sizes of railway networks and do not provide any solution for planning and scheduling of trains over large railway networks having unconstrained numbers of the trains, stations, platforms and multiple track lines. Prior art solutions cannot be extended to address the efficient and effective planning and scheduling for such large railway networks.
Hence there is a need for an online planning method and system that can dynamically react rapidly and efficiently to continuous traffic delays, deviations and disruptions and other conditions on an on-going basis and holistically and reschedule the very large numbers of trains considering the many interactions over the very large railway network having unconstrained number of the trains, stations, platforms and multiple track lines.